| Title: | Lung Cancer Screening Based on Type-different Sensor Arrays |
| Author(s): | Li W; Liu H; Xie D; He Z; Pi X; |
| Address: | "Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, P.R. China. Artificial Intelligence of Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Zigong, Sichuan Province, P.R. China. Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, P.R. China. liuhongying@cqu.edu.cn. Chongqing Engineering Research Center of Medical Electronics, Chongqing, P.R. China. liuhongying@cqu.edu.cn. Chongqing Red Cross Hospital (People's Hospital of Jiangbei District), Chongqing, P.R. China. Key Laboratory of Biorheology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, P.R. China. pixitian@cqu.edu.cn. Key Laboratories for National Defense Science and Technology of Innovative Micro-Nano Devices and System Technology, Chongqing University, Chongqing, P.R. China. pixitian@cqu.edu.cn" |
| DOI: | 10.1038/s41598-017-02154-9 |
| ISSN/ISBN: | 2045-2322 (Electronic) 2045-2322 (Linking) |
| Abstract: | "In recent years, electronic nose (e-nose) systems have become a focus method for diagnosing pulmonary diseases such as lung cancer. However, principles and patterns of sensor responses in traditional e-nose systems are relatively homogeneous. Less study has been focused on type-different sensor arrays. In this paper, we designed a miniature e-nose system using 14 gas sensors of four types and its subsequent analysis of 52 breath samples. To investigate the performance of this system in identifying and distinguishing lung cancer from other respiratory diseases and healthy controls, five feature extraction algorithms and two classifiers were adopted. Lastly, the influence of type-different sensors on the identification ability of e-nose systems was analyzed. Results indicate that when using the LDA fuzzy 5-NN classification method, the sensitivity, specificity and accuracy of discriminating lung cancer patients from healthy controls with e-nose systems are 91.58%, 91.72% and 91.59%, respectively. Our findings also suggest that type-different sensors could significantly increase the diagnostic accuracy of e-nose systems. These results showed e-nose system proposed in this study was potentially practicable in lung cancer screening with a favorable performance. In addition, it is important for type-different sensors to be considered when developing e-nose systems" |
| Keywords: | Biomarkers *Biosensing Techniques/instrumentation/methods Breath Tests/methods Early Detection of Cancer *Electronic Nose Equipment Design Humans Lung Neoplasms/*diagnosis Sensitivity and Specificity Volatile Organic Compounds; |
| Notes: | "MedlineLi, Wang Liu, Hongying Xie, Dandan He, Zichun Pi, Xititan eng Research Support, Non-U.S. Gov't England 2017/05/18 Sci Rep. 2017 May 16; 7(1):1969. doi: 10.1038/s41598-017-02154-9" |
